1. Technical Field
This application generally relates to the field of object detection and, in particular, to detecting the presence of a bicyclist using a hierarchical classifier.
2. Background Information
“Object detection” refers to the task of automatically detecting the presence of an object in a video image or a still image. For example, a detection system may detect the presence of a person or a bicyclist in a still image. As used herein, “bicyclist” refers to the combination of a bicycle and its rider.
Object detection may be used, for example, in vehicles (e.g., automobiles) to increase the safety of the driver of the vehicle, pedestrians, bicyclists, and any other person sharing the road with the vehicle.
Many problems exist with current object detection systems. One problem with object detection systems is the lack of an extensive training set for training the object detection model. A training set, which includes positive samples (images including the object to be detected) and negative samples (images not including the object to be detected), is provided to a machine learning algorithm to produce an object detection model. Positive samples may be available for a limited number of object types (e.g., pedestrians), but positive samples for other types of objects (e.g., bicyclists) may be difficult to find.
Furthermore, when generating a new training set for a certain type of object, the images are manually annotated with certain pieces of information. For example, an indication that the object is present in the image and/or certain parameters of the object (e.g., color of the object and location of the object within the image) may be added to the image. The machine learning algorithm uses those annotations and images to generate a model for detecting the object. The annotation process can be tedious and time consuming.
Additionally, accurately detecting the presence of certain types of objects may be too complex and, thus, may be too slow for real-time applications. For instance, bicyclist recognition is more complex than pedestrian recognition, since variations in appearance due to viewpoint are far more pronounced in bicyclists than in pedestrians. Also, the upper body posture of bicyclists varies more than the posture of typical pedestrians. Moreover, bicyclists move faster and their proximity to vehicles is often much closer. This leads to larger variation in the size of the object and degraded image quality through motion blur and defocusing. The increase in complexity in the detection of a bicyclist compared to the detection of a pedestrian means that most detection systems are not suitable for real-time applications. Thus, certain applications (e.g., bicyclist detection in a vehicle system) may benefit from a faster object recognition scheme.